Ketamine effects on resting state functional brain connectivity in major depressive disorder patients: a hypothesis-driven analysis based on a network model of depression.

Introduction: Ketamine demonstrates robust and rapidly occurring antidepressant effects in patients with difficult-to-treat major depressive disorder. Ketamine's antidepressant effects and its impact on functional networks in non-resistant forms of major depressive disorder are expected to provide valuable insight into ketamine's mechanism of action related to depression.

Methods: This study employs an existing network model of major depressive disorder to investigate the effects of ketamine on resting state connectivity in a therapy-non-resistant major depressive disorder population. In a randomized, double-blind, placebo-controlled, cross-over study, 0.5 mg/kg racemic ketamine or 0.9%NaCl was administered intravenously in 16 MDD patients. We applied resting-state functional magnetic resonance imaging (rs-fMRI) to explore changes in functional brain connectivity directly at 50, 80 and 165 min (acute) and 24 h (delayed) following ketamine administration. A clinician-rated 10-item scale (MADRS) was administered at 165 min and 24 h after ketamine administration. Connections-of-interest (COIs) were based on the previously published corticolimbic-insular-striatalpallidal-thalamic (CLIPST) circuitry model of major depressive disorder.

Results: Compared with placebo, ketamine significantly (p < 0.0014) reduced the mean (SD) MADRS total score from 21.2 (5.9) pre-dose to 10.3 (4.6) 24 h post-dose. At both acute (p < 0.0172) and delayed (p < 0.0488) time points, significant rs-fMRI connectivity changes occurred only in MDD-related COIs as proposed by the CLIPST model. No changes in functional connectivity were found in non-CLIPST connections.

Discussion: This study demonstrates that ketamine specifically affects depression-related circuitry. Analyzing functional connectivity based on a neurocircuitry model of a specific CNS disease and drug action may be an effective approach that could result in a more targeted analysis in future pharmaco-fMRI studies in CNS drug development.